Influence of macrophyte species on microbial density and activity in constructed wetlands
Bibliographic record
Abstract
It is often assumed that planted wastewater treatment systems outperform unplanted ones, mainly because plants stimulate belowground microbial population. Yet, fundamental interactions between plants and associated microorganisms remain only partly understood. The aim of our project was to evaluate microbial density and activity associated to the rhizosphere of three plant species. Experimental set-up, in six replicates, consisted of four 1.8-L microcosms respectively planted in monoculture of Typha angustifolia, Phragmites australis, Phalaris arundinacea and unplanted control. Plants were grown for two months with 25 L m(-2) d(-1) of secondary effluent (in g m(-2) d(-1): 1.3 TSS, 7.5 COD, 1.0 TKN). Sampling of substrate, roots and interstitial water was made according to depth (0-10, 10-20 cm). Biofilm was extracted with 500 mL of a buffer solution. Microbial density was directly estimated by flow cytometry and indirectly by protein measurements. Biological activity was determined using respirometry assays, dehydrogenase and enzymatic activity measurements. Our results show that microbial density and activity are higher in the presence of plants, with significantly higher values associated with Phalaris arundinacea. Greater density of aerobic or facultative bacteria was present in planted microcosm, particularly on root surface, suggesting root oxygen release. Microbes were present on substrate and roots as an attached biofilm and abundance was correlated to root surface throughout depth. Plant species root morphology and development seem to be a key factor influencing microbial-plant interaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".